Transcript Introduction to SQL - University of Utah
IS 4420 Database Fundamentals Chapter 7: Introduction to SQL Leon Chen
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Systems Development Life Cycle Project Identification and Selection Project Initiation and Planning Analysis Logical Design Physical Design Implementation Maintenance Database Development Process Enterprise modeling Conceptual data modeling Logical database design Physical database design and definition Database implementation Database maintenance 2
Part Four: Implementation
Chapter 7 – Introduction to SQL Chapter 8 – Advanced SQL Chapter 9 – Client/Server Environment Chapter 10 – Internet Chapter 11 – Data Warehousing 3
Overview
Define a database using SQL data definition language Work with Views Write single table queries Establish referential integrity 4
SQL Overview
Structured Query Language The standard for relational database management systems (RDBMS) SQL-92 and SQL-99 Standards – Purpose: Specify syntax/semantics for data definition and manipulation Define data structures Enable portability Specify minimal (level 1) and complete (level 2) standards Allow for later growth/enhancement to standard 5
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SQL Environment
Catalog A set of schemas that constitute the description of a database Schema The structure that contains descriptions of objects created by a user (base tables, views, constraints) Data Definition Language (DDL) Commands that define a database, including creating, altering, and dropping tables and establishing constraints Data Manipulation Language (DML) Commands that maintain and query a database Data Control Language (DCL) Commands that control a database, including administering privileges and committing data 7
SQL Data types (from Oracle 9i)
String types CHAR (n) – fixed-length character data, n characters long Maximum length = 2000 bytes VARCHAR2 (n) – variable length character data, maximum 4000 bytes LONG – variable-length character data, up to 4GB. Maximum 1 per table Numeric types NUMBER (p,q) – general purpose numeric data type INTEGER (p) – signed integer, p digits wide FLOAT (p) – floating point in scientific notation with p binary digits precision Date/time type DATE – fixed-length date/time in dd-mm-yy form 8
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SQL Database Definition
Data Definition Language (DDL) Major CREATE statements: CREATE SCHEMA – defines a portion of the database owned by a particular user CREATE TABLE – defines a table and its columns CREATE VIEW – defines a logical table from one or more views Other CREATE statements: CHARACTER SET, COLLATION, TRANSLATION, ASSERTION, DOMAIN 10
The following slides create tables for this enterprise data model
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Relational Data Model 12
Create PRODUCT table Non-nullable specification
Primary keys can never have NULL values
Identifying primary key
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Non-nullable specifications Primary key Some primary keys are composite – composed of multiple attributes 14
Controlling the values in attributes
Default value Domain constraint 15
Identifying foreign keys and establishing relationships Primary key of parent table Foreign key of dependent table 16
Data Integrity Controls
Referential integrity – constraint that ensures that foreign key values of a table must match primary key values of a related table in 1:M relationships Restricting: Deletes of primary records Updates of primary records Inserts of dependent records 17
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Using and Defining Views
Views provide users controlled access to tables Base Table – table containing the raw data Dynamic View A “virtual table” created dynamically upon request by a user No data actually stored; instead data from base table made available to user Materialized View Copy or replication of data Data actually stored Based on SQL SELECT statement on base tables or other views Must be refreshed periodically to match the corresponding base tables 19
Sample CREATE VIEW
CREATE VIEW EXPENSIVE_STUFF_V AS SELECT PRODUCT_ID, PRODUCT_NAME, UNIT_PRICE FROM PRODUCT_T WHERE UNIT_PRICE >300 WITH CHECK_OPTION; View has a name View is based on a SELECT statement CHECK_OPTION works only for updateable views and prevents updates that would create rows not included in the view 20
Advantages of Views
Simplify query commands Assist with data security (but don't rely on views for security, there are more important security measures) Enhance programming productivity Contain most current base table data Use little storage space Provide customized view for user Establish physical data independence 21
Disadvantages of Views
Use processing time each time view is referenced May or may not be directly updateable 22
Create Four Views
CREATE VIEW CUSTOMER_V AS SELECT * FROM CUSTOMER_T; CREATE VIEW ORDER_V AS SELECT * FROM ORDER_T; CREATE VIEW ORDER_LINE_V AS SELECT * FROM ORDER_LINE_T; CREATE VIEW PRODUCT_V AS SELECT * FROM PRODUCT_T; ‘ * ’ is the wildcard 23
Changing and Removing Tables
ALTER TABLE statement allows you to change column specifications: ALTER TABLE CUSTOMER_T ADD (TYPE VARCHAR(2)) DROP TABLE statement allows you to remove tables from your schema: DROP TABLE CUSTOMER_T 24
Schema Definition
Control processing/storage efficiency: Choice of indexes File organizations for base tables File organizations for indexes Data clustering Statistics maintenance Creating indexes Speed up random/sequential access to base table data Example CREATE INDEX NAME_IDX ON CUSTOMER_T(CUSTOMER_NAME) This makes an index for the CUSTOMER_NAME field of the CUSTOMER_T table 25
Insert Statement
Adds data to a table Inserting a record with all fields INSERT INTO CUSTOMER_T VALUES (001, ‘Contemporary Casuals’, 1355 S. Himes Blvd.’, ‘Gainesville’, ‘FL’, 32601); Inserting a record with specified fields INSERT INTO PRODUCT_T (PRODUCT_ID, PRODUCT_DESCRIPTION, PRODUCT_FINISH, STANDARD_PRICE, PRODUCT_ON_HAND) VALUES (1, ‘End Table’, ‘Cherry’, 175, 8); Inserting records from another table INSERT INTO CA_CUSTOMER_T SELECT * FROM CUSTOMER_T WHERE STATE = ‘CA’; 26
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Delete Statement
Removes rows from a table Delete certain rows DELETE FROM CUSTOMER_T WHERE STATE = ‘HI’; Delete all rows DELETE FROM CUSTOMER_T; 31
Update Statement
Modifies data in existing rows UPDATE PRODUCT_T SET UNIT_PRICE = 775 WHERE PRODUCT_ID = 7; 32
SELECT Statement
Used for queries on single or multiple tables Clauses of the SELECT statement: SELECT List the
columns
FROM (and expressions) that should be returned from the query Indicate the
table
(s) or view(s) from which data will be obtained WHERE Indicate the
conditions
GROUP BY Indicate
columns
under which a row will be included in the result to group the results HAVING Indicate the
conditions
under which a group will be included ORDER BY Sorts the result according to specified
columns
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Figure 7-8: SQL statement processing order 34
SELECT Example
Find products with standard price less than $275 SELECT FROM PRODUCT_NAME, STANDARD_PRICE PRODUCT_V WHERE STANDARD_PRICE < 275;
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SELECT Example using Alias
Alias is an alternative column or table name SELECT CUST .CUSTOMER AS NAME , CUST.CUSTOMER_ADDRESS FROM CUSTOMER_V CUST WHERE NAME = ‘Home Furnishings’; 37
SELECT Example Using a Function
Using the COUNT find totals
aggregate function
to Aggregate functions: SUM(), MIN(), MAX(), AVG(), COUNT() SELECT COUNT(*) FROM ORDER_LINE_V WHERE ORDER_ID = 1004;
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SELECT Example – Boolean Operators
AND , OR , and NOT Operators for customizing conditions in WHERE clause SELECT PRODUCT_DESCRIPTION, PRODUCT_FINISH, STANDARD_PRICE FROM PRODUCT_V WHERE (PRODUCT_DESCRIPTION LIKE OR PRODUCT_DESCRIPTION LIKE ‘ % ‘ % Desk’ Table’) AND UNIT_PRICE > 300; Note: the LIKE operator allows you to compare strings using wildcards. For example, the % wildcard in ‘%Desk’ indicates that all strings that have any number of characters preceding the word “Desk” will be allowed 39
SELECT Example – Sorting Results with the ORDER BY Clause
Sort the results first by STATE, and within a state by CUSTOMER_NAME SELECT CUSTOMER_NAME, CITY, STATE FROM CUSTOMER_V WHERE STATE
IN
(‘FL’, ‘TX’, ‘CA’, ‘HI’) ORDER BY STATE, CUSTOMER_NAME; Note: the IN operator in this example allows you to include rows whose STATE value is either FL, TX, CA, or HI. It is more efficient than separate OR conditions 40
SELECT Example –
Categorizing Results Using the GROUP BY Clause SELECT STATE, COUNT(STATE) FROM CUSTOMER_V
GROUP BY
STATE; Note: you can use single-value fields with aggregate functions if they are included in the GROUP BY clause
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SELECT Example –
Qualifying Results by Categories Using the HAVING Clause For use with GROUP BY SELECT STATE, COUNT(STATE) FROM CUSTOMER_V GROUP BY STATE
HAVING
COUNT(STATE) > 1; Like a WHERE clause, but it operates on groups (categories), not on individual rows. Here, only those groups with total numbers greater than 1 will be included in final result 42